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Poster De Conférence Année : 2021

Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection

Lucie Tamisier
Thierry Candresse

Résumé

In the last decade, High-Throughput Sequencing (HTS) has revolutionized plant virus discovery and diagnosis. Currently, many bioinformatics pipelines for virus detection are available, making the choice of a suitable one difficult. A robust benchmarking is needed for the unbiased comparison of the pipelines. We present 7 semi-artificial datasets composed of real RNA-seq datasets from virus-infected plants spiked with artificial virus reads. Each dataset addresses challenges that could prevent virus detection (i.e. low viral concentration, new viral species, non-complete virus genome, etc). We also present 3 real datasets showing a challenging virus composition as well as 8 completely artificial datasets to test haplotype reconstruction software. With these datasets, we hope to encourage virologists, diagnosticians and bioinformaticians to evaluate and benchmark their pipeline(s).
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Dates et versions

hal-04315151 , version 1 (30-11-2023)

Identifiants

  • HAL Id : hal-04315151 , version 1

Citer

Lucie Tamisier, Annelies Haegeman, Yoika Foucart, Nicolas Fouillien, Maher Al Rwahnih, et al.. Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection. International Advances in Plant Virology, Apr 2021, Avignon (en ligne), France. ⟨hal-04315151⟩
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